(6) #310 Carleton College-Karls (7-11)

298.44 (19)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
262 Minnesota-Duluth Loss 4-10 -18.16 30 4.82% Counts (Why) Mar 5th Midwest Throwdown 2022
192 Wisconsin-Eau Claire Loss 2-10 -4.84 30 4.82% Counts (Why) Mar 5th Midwest Throwdown 2022
317 Washington University-B Win 8-7 4.63 28 4.9% Counts Mar 5th Midwest Throwdown 2022
222 Luther Loss 7-11 -3.35 69 5.37% Counts Mar 6th Midwest Throwdown 2022
253 St John's Loss 9-13 -6.58 120 5.52% Counts Mar 6th Midwest Throwdown 2022
317 Washington University-B Win 11-9 12.5 28 5.52% Counts Mar 6th Midwest Throwdown 2022
345 Illinois-B Win 10-6 14.01 0 5.06% Counts (Why) Mar 6th Midwest Throwdown 2022
222 Luther Loss 6-13 -12.68 69 6.19% Counts (Why) Mar 19th College Southerns XX
195 Georgia College Loss 7-13 -3.91 13 6.19% Counts Mar 19th College Southerns XX
274 Georgia Southern Loss 6-13 -26.43 13 6.19% Counts (Why) Mar 19th College Southerns XX
205 Georgia Tech-B Loss 8-12 1.02 29 6.19% Counts Mar 20th College Southerns XX
335 Florida-B Loss 9-10 -18.54 21 6.19% Counts Mar 20th College Southerns XX
224 Minnesota-B Loss 10-13 6.23 26 8.26% Counts Apr 23rd North Central Dev College Mens CC 2022
299 Minnesota-C Loss 10-11 -4.15 33 8.26% Counts Apr 23rd North Central Dev College Mens CC 2022
347 Iowa State-B Win 13-6 30.4 32 8.26% Counts (Why) Apr 23rd North Central Dev College Mens CC 2022
374 Wisconsin-Eau Claire-B** Win 13-4 0 27 0% Ignored (Why) Apr 23rd North Central Dev College Mens CC 2022
267 Wisconsin-B Win 11-10 31.35 28 8.26% Counts Apr 24th North Central Dev College Mens CC 2022
375 Wisconsin-Milwaukee-B** Win 13-2 0 27 0% Ignored (Why) Apr 24th North Central Dev College Mens CC 2022
**Blowout Eligible. Learn more about how this works here.

FAQ

The results on this page ("USAU") are the results of an implementation of the USA Ultimate Top 20 algorithm, which is used to allocate post season bids to both colleg and club ultimate teams. The data was obtained by scraping USAU's score reporting website. Learn more about the algorithm here. TL;DR, here is the rating function. Every game a team plays gets a rating equal to the opponents rating +/- the score value. With all these data points, we iterate team ratings until convergence. There is also a rule for discounting blowout games (see next FAQ)
For reference, here is handy table with frequent game scrores and the resulting game value:
"...if a team is rated more than 600 points higher than its opponent, and wins with a score that is more than twice the losing score plus one, the game is ignored for ratings purposes. However, this is only done if the winning team has at least N other results that are not being ignored, where N=5."

Translation: if a team plays a game where even earning the max point win would hurt them, they can have the game ignored provided they win by enough and have suffficient unignored results.